





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase :			Tuple     		=    logging.get_logger(__name__)

lowercase :			Dict     		=    {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 					A__       (				__lowercase      ):




   """simple docstring"""



   __A  :							List[str]			    =    '''sew'''

   def __init__(   self   ,    lowercase=32   ,    lowercase=768   ,    lowercase=12   ,    lowercase=12   ,    lowercase=3072   ,    lowercase=2   ,    lowercase="gelu"   ,    lowercase=0.1   ,    lowercase=0.1   ,    lowercase=0.1   ,    lowercase=0.0   ,    lowercase=0.1   ,    lowercase=0.1   ,    lowercase=0.02   ,    lowercase=1e-5   ,    lowercase="group"   ,    lowercase="gelu"   ,    lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,    lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,    lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,    lowercase=False   ,    lowercase=128   ,    lowercase=16   ,    lowercase=True   ,    lowercase=0.05   ,    lowercase=10   ,    lowercase=2   ,    lowercase=0.0   ,    lowercase=10   ,    lowercase=0   ,    lowercase="mean"   ,    lowercase=False   ,    lowercase=False   ,    lowercase=256   ,    lowercase=0   ,    lowercase=1   ,    lowercase=2   ,    **lowercase   ,    )     ->		List[Any]:







          '''simple docstring'''

          super().__init__(**lowercase   ,    pad_token_id=lowercase   ,    bos_token_id=lowercase   ,    eos_token_id=lowercase)
          a__		:      Dict					=							hidden_size
          a__		:      List[str]					=							feat_extract_norm
          a__		:      List[str]					=							feat_extract_activation
          a__		:      Any					=							list(lowercase)
          a__		:      Optional[int]					=							list(lowercase)
          a__		:      str					=							list(lowercase)
          a__		:      Optional[int]					=							conv_bias
          a__		:      Dict					=							num_conv_pos_embeddings
          a__		:      str					=							num_conv_pos_embedding_groups
          a__		:      Optional[int]					=							len(self.conv_dim)
          a__		:      List[str]					=							num_hidden_layers
          a__		:      List[Any]					=							intermediate_size
          a__		:      int					=							squeeze_factor
          a__		:      List[str]					=							hidden_act
          a__		:      int					=							num_attention_heads
          a__		:      Any					=							hidden_dropout
          a__		:      List[Any]					=							attention_dropout
          a__		:      Union[str, Any]					=							activation_dropout
          a__		:      Dict					=							feat_proj_dropout
          a__		:      Tuple					=							final_dropout
          a__		:      Union[str, Any]					=							layerdrop
          a__		:      Tuple					=							layer_norm_eps
          a__		:      Optional[Any]					=							initializer_range
          a__		:      Optional[Any]					=							vocab_size

          if (
              (len(self.conv_stride) != self.num_feat_extract_layers)
              or (len(self.conv_kernel) != self.num_feat_extract_layers)
              or (len(self.conv_dim) != self.num_feat_extract_layers)
          ):
                 raise ValueError(
                     'Configuration for convolutional layers is incorrect.'
                     'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                     F'but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)'
                     F'= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.')

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          a__		:      Dict					=							apply_spec_augment
          a__		:      Union[str, Any]					=							mask_time_prob
          a__		:      Any					=							mask_time_length
          a__		:      List[str]					=							mask_time_min_masks
          a__		:      int					=							mask_feature_prob
          a__		:      Optional[Any]					=							mask_feature_length
          a__		:      Any					=							mask_feature_min_masks

          # ctc loss
          a__		:      Dict					=							ctc_loss_reduction
          a__		:      List[str]					=							ctc_zero_infinity

          # sequence classification
          a__		:      Optional[Any]					=							use_weighted_layer_sum
          a__		:      str					=							classifier_proj_size

   @property
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          return functools.reduce(operator.mul   ,    self.conv_stride   ,    1)




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    :   Tuple						=			logging.getLogger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:Union[str, Any]=-1		)						->					List[str]:
								# in NER datasets, the last column is usually reserved for NER label
								__UpperCamelCase      :  str       =   label_idx







	def   _lowerCamelCase							(   self						:str	,  a						:Tuple	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Dict       =   mode.value
								__UpperCamelCase      :  Union[str, Any]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Any       =   1
								__UpperCamelCase      :  List[str]       =   []
								with open(a	,  encoding="utf-8"		) as f:
															__UpperCamelCase      :  Tuple       =   []
															__UpperCamelCase      :  Dict       =   []
															for line in f:
																						if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																													if words:
																																				examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																																				guid_index += 1
																																				__UpperCamelCase      :  Dict       =   []
																																				__UpperCamelCase      :  List[str]       =   []
																						else:
																													__UpperCamelCase      :  Union[str, Any]       =   line.split(" "		)
																													words.append(splits[0]		)
																													if len(a		) > 1:
																																				labels.append(splits[self.label_idx].replace("\n"	,  ""		)		)
																													else:
																																				# Examples could have no label for mode = "test"
																																				labels.append("O"		)
															if words:
																						examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
								return examples







	def   _lowerCamelCase							(   self						:int	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					Optional[Any]:
								__UpperCamelCase      :  Tuple       =   0
								for line in test_input_reader:
															if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																						writer.write(a		)
																						if not preds_list[example_id]:
																													example_id += 1
															elif preds_list[example_id]:
																						__UpperCamelCase      :  Union[str, Any]       =   line.split()[0] + " " + preds_list[example_id].pop(0		) + "\n"
																						writer.write(a		)
															else:
																						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,  line.split()[0]		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  List[str]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  Any       =   ["O"] + labels
															return labels
								else:
															return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:List[str]		)						->					int:
								# in CONLL2003 dataset chunk column is second-to-last
								super().__init__(label_idx=-2		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  Optional[Any]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  int       =   ["O"] + labels
															return labels
								else:
															return [
															    "O",
															    "B-ADVP",
															    "B-INTJ",
															    "B-LST",
															    "B-PRT",
															    "B-NP",
															    "B-SBAR",
															    "B-VP",
															    "B-ADJP",
															    "B-CONJP",
															    "B-PP",
															    "I-ADVP",
															    "I-INTJ",
															    "I-LST",
															    "I-PRT",
															    "I-NP",
															    "I-SBAR",
															    "I-VP",
															    "I-ADJP",
															    "I-CONJP",
															    "I-PP",
															]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, Any]	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   mode.value
								__UpperCamelCase      :  List[str]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Dict       =   1
								__UpperCamelCase      :  List[str]       =   []

								with open(a	,  encoding="utf-8"		) as f:
															for sentence in parse_incr(a		):
																						__UpperCamelCase      :  Optional[int]       =   []
																						__UpperCamelCase      :  int       =   []
																						for token in sentence:
																													words.append(token["form"]		)
																													labels.append(token["upos"]		)
																						assert len(a		) == len(a		)
																						if words:
																													examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																													guid_index += 1
								return examples







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					str:
								__UpperCamelCase      :  List[Any]       =   0
								for sentence in parse_incr(a		):
															__UpperCamelCase      :  Tuple       =   preds_list[example_id]
															__UpperCamelCase      :  Dict       =   ""
															for token in sentence:
																						out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0		)}) '
															out += "\n"
															writer.write(a		)
															example_id += 1







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						return f.read().splitlines()
								else:
															return [
															    "ADJ",
															    "ADP",
															    "ADV",
															    "AUX",
															    "CCONJ",
															    "DET",
															    "INTJ",
															    "NOUN",
															    "NUM",
															    "PART",
															    "PRON",
															    "PROPN",
															    "PUNCT",
															    "SCONJ",
															    "SYM",
															    "VERB",
															    "X",
															]